#%pip install ydata_profiling
#pip install --upgrade pandas_profiling
#pip install numpy==1.19.3
#pip install pandas
#pip show pandas
#pip install openpyxl

#pip install numpy

#pip install ipywidgets

import pandas as pd
from ydata_profiling import ProfileReport
from pathlib import Path
from django.db.models import Max
# from pydantic_settings import BaseSettings
#from pandas_profiling import ProfileReport

# Your other code here

import os
os.environ["DJANGO_ALLOW_ASYNC_UNSAFE"] = "true"

from DataTune.models import UploadedData

#data = UploadedData.objects.get(id=1)
latest_uploaded_data = UploadedData.objects.latest('uploaded_at')

#data = UploadedData.objects.latest('uploaded_at')

# data = pd.read_csv(UploadedData.objects.all())
with open(latest_uploaded_data.file.path, 'rb') as file:
    content = file.read()
file_path = latest_uploaded_data.file.path
    
# if file_path.endswith('csv'):
#     df = pd.read_csv(file_path)
# elif file_path.endswith('.xls','.xlsx'):
#     df = pd.read_excel(file_path, engine = 'openpyxl')
# # df = pd.DataFrame.from_records(content)
if file_path.endswith(('csv', '.xls', '.xlsx')):
    if file_path.endswith('.csv'):
        #df = pd.read_csv(file_path, header=None)
        df = pd.read_csv(file_path, delimiter=',', encoding='utf-8',skiprows=4)
    else:
        df = pd.read_excel(file_path, engine='openpyxl')
else:
    # Handle unsupported file format or raise an exception
    raise ValueError("Unsupported file format. Only CSV, XLS, and XLSX are supported.")

df.head()
# content

	WineClass	Alcohol	Malic acid	Ash	Alcalinity of ash	Magnesium	Total phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color intensity	Hue	OD280/OD315 of diluted wines	Proline
0	3	12.85	3.27	2.58	22.0	106.0	1.65	0.60	0.60	0.96	5.58	0.87	2.11	570.0
1	2	12.64	1.36	2.02	16.8	100.0	2.02	1.41	0.53	0.62	5.75	0.98	1.59	450.0
2	2	12.08	1.13	2.51	24.0	78.0	2.00	1.58	0.40	1.40	2.20	1.31	2.72	630.0
3	2	12.00	1.51	2.42	22.0	86.0	1.45	1.25	0.50	1.63	3.60	1.05	2.65	450.0
4	1	13.64	3.10	2.56	15.2	116.0	2.70	3.03	0.17	1.66	5.10	0.96	3.36	845.0

# pip install pandas-profiling

profile = ProfileReport(df, title = 'Pandas Profiling EDA Report', explorative = True)

profile.to_notebook_iframe()

# Extract the original filename from the UploadedData model
original_filename = latest_uploaded_data.file.name.split('.')[0]  # Assuming the file is stored in a subdirectory

# Remove the file extension (if any)
filename_without_extension = Path(original_filename).stem

# file_path = "..\media\EDA_report\pandas_profiling_report.html"
# profile.to_file(file_path)
file_path = Path("..") / "media" / "EDA_report" / f"pandas_profiling_report_{filename_without_extension}.html"

# Ensure that the directory exists, and create it if not
file_path.parent.mkdir(parents=True, exist_ok=True)

# Save the report as an HTML file
profile.to_file(file_path)

# %%capture captured_output
# %%HTML
# <h1>My EDA Report</h1>
# <p>This is some content of your EDA report.
# profile.to_notebook_iframe()
# </p>
# with open('C:\Users\Sristi\DataTune_app\DataTune_app\media\EDA_report\eda_report.html', 'w') as file:
#     file.write(captured_output.stdout)

pip install lazypredict

Requirement already satisfied: lazypredict in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (0.2.12)
Requirement already satisfied: click in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (8.1.7)
Requirement already satisfied: scikit-learn in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (1.4.0)
Requirement already satisfied: pandas in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (2.2.0)
Requirement already satisfied: tqdm in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (4.66.1)
Requirement already satisfied: joblib in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (1.3.2)
Requirement already satisfied: lightgbm in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (4.3.0)
Requirement already satisfied: xgboost in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lazypredict) (2.0.3)
Requirement already satisfied: colorama in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from click->lazypredict) (0.4.6)
Requirement already satisfied: numpy in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lightgbm->lazypredict) (1.25.2)
Requirement already satisfied: scipy in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from lightgbm->lazypredict) (1.11.4)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from pandas->lazypredict) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from pandas->lazypredict) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from pandas->lazypredict) (2023.4)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from scikit-learn->lazypredict) (3.2.0)
Requirement already satisfied: six>=1.5 in c:\users\sristi\datatune_app\datatune_app\.venv\lib\site-packages (from python-dateutil>=2.8.2->pandas->lazypredict) (1.16.0)
Note: you may need to restart the kernel to use updated packages.

[notice] A new release of pip is available: 23.3.2 -> 24.0
[notice] To update, run: python.exe -m pip install --upgrade pip

import lazypredict